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ecology, remote sensing, and computer science. UAV imagery and increasingly sophisticated and targeted AI algorithms can estimate forage quality, biodiversity, and the abundance of individual plant species
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-Informed Neural Networks (PINNs) and hybrid models that respect the physical laws governing the real-world system Applying Deep Reinforcement Learning (DRL) algorithms to optimize processes within simulation
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sensors, laboratory and greenhouse space, as well as cloud and supercomputing resources. We also engage closely with researchers at University of California campuses, especially those in Davis. Learning
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collecting soil, plant, and water samples; monitoring soil moisture with advanced sensors; organizing and processing research data; and contributing to modeling efforts that examine water and nutrient dynamics
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inference) Algorithmic development for bilevel (or multilevel) optimization Methodological developments in Bayesian statistics and/or decision analysis Application of adversarial risk analysis within security
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variety of meteorological and snowpack sensors. Learn to document avalanche activity and environmental conditions using standardized protocols. Data Analysis and Modeling Train to process and analyze